Observe Performance Cookbook¶
Working directly in OPAL allows a wider range of options when modeling data. The following recommendations may give you better performance from your OPAL pipelines.
Optimizing Observe Performance
- Observe Performance Cookbook: Use Approximate Values When Feasible
- Observe Performance Cookbook: Avoid Large JSON Blobs
- Observe Performance Cookbook: Cast Data Columns Extracted from JSON
- Observe Performance Cookbook: Create Intermediate Datasets
- Observe Performance Cookbook: Filter Earlier in OPAL Scripts
- Observe Performance Cookbook: Using Filter instead of Ever
- Observe Performance Cookbook: Flatten Less First
- Observe Performance Cookbook: Limit Worksheet Time Windows
- Observe Performance Cookbook: Limit Resource Time Windows
- Observe Performance Cookbook: Limit Valid Event Time Windows
- Observe Performance Cookbook: Look for Hidden Columns
- Observe Performance Cookbook: Use Make_Events before Window Functions
- Observe Performance Cookbook: Mark Immutable Resource Columns
- Observe Performance Cookbook: Making Resources from Multiple Datasets
- Observe Performance Cookbook: Prefer Join to Lookup
- Observe Performance Cookbook: Prefer Lead and Lag to First and Last
- Observe Performance Cookbook: Prefer Timechart to Timestats
- Observe Performance Cookbook: Limit Query Time Windows
- Observe Performance Cookbook: Limit Query Time Windows
- Observe Performance Cookbook: Reduce Columns Earlier in OPAL Scripts
- Observe Performance Cookbook: Extract from JSON instead of using Flatten
- Observe Performance Cookbook: Type Data Columns
- Observe Performance Cookbook: Use Interval for Ephemeral Things